Cách cài đặt Tensorflow Object Detection API. Ví dụ sử dụng Tensorflow Object Detection API

          Chào các bạn bài này mình sẽ hướng dẫn các bạn cách cài đặt Tensorflow trên máy tính WIndows. Sau đó chúng ta sẽ sử dụng Object detection API để làm một ví dụ về nhận dạng đối tượng.

         Những phần mềm cần có trước khi cài: Python 64bit + PIP
Bước 1: Sử dụng PIP cài đặt tensorflow. Nếu bạn nào chưa cài PIP thì các bạn cần cài đặt PI trước.
-Thường PIP sẽ nằm tại thư mục ~/Python35/Scrpits/ [Ví dụ: C:\Users\h2phamth\AppData\Local\Programs\Python\Python35\Scripts]

-Lệnh cài:

# For CPU
pip install tensorflow
# For GPU
pip install tensorflow-gpu

       

Bước 2: Download model source code trên github và protocol code sau đó giải nén ra
Link model: https://github.com/tensorflow/models/
Link protol: https://github.com/google/protobuf/releases

Bước 3: Tiến hành cài đặt

# Từ tensorflow/models/research/
protoc object_detection/protos/*.proto --python_out=.

Bước 4: Dùng jupyter để mở ví dụ có sẵn.

Bước 5: Mở file ví dụ có sẵn tại: (C:\tensorflow\models-master\research\object_detection\object_detection_tutorial.ipynb) Sau đó chuyển dổi sang đuôi .py

Bước 6: Chỉnh sửa một số code để dụng Camera của máy hoặc load file video từ máy tính.

# coding: utf-8 # # Object Detection Demo # Welcome to the object detection inference walkthrough! This notebook will walk you step by step through the process of using a pre-trained model to detect objects in an image. Make sure to follow the [installation instructions](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md) before you start. # # Imports # In[ ]: import numpy as np import os import six.moves.urllib as urllib import sys import tarfile import tensorflow as tf import zipfile import cv2 from collections import defaultdict from io import StringIO from matplotlib import pyplot as plt from PIL import Image cap = cv2.VideoCapture("video.mp4") # This is needed since the notebook is stored in the object_detection folder. sys.path.append("..") # ## Object detection imports # Here are the imports from the object detection module. # In[ ]: from utils import label_map_util from utils import visualization_utils as vis_util # # Model preparation # ## Variables # # Any model exported using the `export_inference_graph.py` tool can be loaded here simply by changing `PATH_TO_CKPT` to point to a new .pb file. # # By default we use an "SSD with Mobilenet" model here. See the [detection model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md) for a list of other models that can be run out-of-the-box with varying speeds and accuracies. # In[ ]: # What model to download. MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17' MODEL_FILE = MODEL_NAME + '.tar.gz' DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/' # Path to frozen detection graph. This is the actual model that is used for the object detection. PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb' # List of the strings that is used to add correct label for each box. PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt') NUM_CLASSES = 90 # ## Download Model # In[ ]: opener = urllib.request.URLopener() opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE) tar_file = tarfile.open(MODEL_FILE) for file in tar_file.getmembers(): file_name = os.path.basename(file.name) if 'frozen_inference_graph.pb' in file_name: tar_file.extract(file, os.getcwd()) # ## Load a (frozen) Tensorflow model into memory. # In[ ]: detection_graph = tf.Graph() with detection_graph.as_default(): od_graph_def = tf.GraphDef() with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: serialized_graph = fid.read() od_graph_def.ParseFromString(serialized_graph) tf.import_graph_def(od_graph_def, name='') # ## Loading label map # Label maps map indices to category names, so that when our convolution network predicts `5`, we know that this corresponds to `airplane`. Here we use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine # In[ ]: label_map = label_map_util.load_labelmap(PATH_TO_LABELS) categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True) category_index = label_map_util.create_category_index(categories) # ## Helper code # In[ ]: def load_image_into_numpy_array(image): (im_width, im_height) = image.size return np.array(image.getdata()).reshape( (im_height, im_width, 3)).astype(np.uint8) # # Detection # In[ ]: # For the sake of simplicity we will use only 2 images: # image1.jpg # image2.jpg # If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS. PATH_TO_TEST_IMAGES_DIR = 'test_images' TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 3) ] # Size, in inches, of the output images. IMAGE_SIZE = (12, 8) # In[ ]: with detection_graph.as_default(): with tf.Session(graph=detection_graph) as sess: # Definite input and output Tensors for detection_graph image_tensor = detection_graph.get_tensor_by_name('image_tensor:0') # Each box represents a part of the image where a particular object was detected. detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0') # Each score represent how level of confidence for each of the objects. # Score is shown on the result image, together with the class label. detection_scores = detection_graph.get_tensor_by_name('detection_scores:0') detection_classes = detection_graph.get_tensor_by_name('detection_classes:0') num_detections = detection_graph.get_tensor_by_name('num_detections:0') while True: ret, image_np = cap.read() # Expand dimensions since the model expects images to have shape: [1, None, None, 3] image_np_expanded = np.expand_dims(image_np, axis=0) # Actual detection. (boxes, scores, classes, num) = sess.run( [detection_boxes, detection_scores, detection_classes, num_detections], feed_dict={image_tensor: image_np_expanded}) # Visualization of the results of a detection. vis_util.visualize_boxes_and_labels_on_image_array( image_np, np.squeeze(boxes), np.squeeze(classes).astype(np.int32), np.squeeze(scores), category_index, use_normalized_coordinates=True, line_thickness=8) cv2.imshow('Obj detection',cv2.resize(image_np,(800,400))) if cv2.waitKey(25)& 0xFF == ord('q'): cv2.destroyAllWindows() cap.release() break # In[ ]:

Video Hướng Dẫn (Bật Phụ Đề Tiếng Việt)

COCO-trained models {#coco-models}: You can use best one which suitable with your program

Model name	Speed (ms)	COCO mAP[^1]	Outputs
ssd_mobilenet_v1_coco	30	21	Boxes
ssd_inception_v2_coco	42	24	Boxes
faster_rcnn_inception_v2_coco	58	28	Boxes
faster_rcnn_resnet50_coco	89	30	Boxes
faster_rcnn_resnet50_lowproposals_coco	64		Boxes
rfcn_resnet101_coco	92	30	Boxes
faster_rcnn_resnet101_coco	106	32	Boxes
faster_rcnn_resnet101_lowproposals_coco	82		Boxes
faster_rcnn_inception_resnet_v2_atrous_coco	620	37	Boxes
faster_rcnn_inception_resnet_v2_atrous_lowproposals_coco	241		Boxes
faster_rcnn_nas	1833	43	Boxes
faster_rcnn_nas_lowproposals_coco

Kitti-trained models {#kitti-models}

Model name	Speed (ms)	Pascal mAP@0.5 (ms)	Outputs
faster_rcnn_resnet101_kitti	79	87	Boxes

Open Images-trained models {#open-images-models}

Model name	Speed (ms)	Open Images mAP@0.5[^2]	Outputs
faster_rcnn_inception_resnet_v2_atrous_oid	727	37	Boxes
faster_rcnn_inception_resnet_v2_atrous_lowproposals_oid	347		Boxe